Seems like everywhere you turn one hears about the replication crisis in psychology (and science in general). We discussed the subject recently - and here is a short introductury presentation I made on the subject to promote the discussion.
Deep learning is everywhere. I find the subject of deep-learning to be one of the most fascinating subjects in science\technology these days. One specific topic which is gaining popularity is the similarities and differences between biological and artificial intelligence. Since deep-learning uses a similar terminology to that of neuroscience (i.e. information processing via a network of connected neurons) the comparison between the disciplines seems natural. A few examples for interesting questions under this topic are:"Does the brain implement deep learning?" if so, "Could the layers of a deep-networks be implemented in the brain by single neurons? single brain-areas?", "Does the brain learn like deep-artificial networks?" , "Do deep-artificial-networks have similar 'behavioral' characteristics as biological networks (e.g. do they also experience optical illusions?)"
I recently gave a talk on this topic. The reading list for the talk (here) is a great start for anyone interested in the subject. The list could be roughly divided into two parts: (1) introduction to deep learning; the resources for this part are either text-book material or somtimes based on recent reports and opinions that are often published in blogs and other non-official releases, rather than in scientific journals. (2) deep-learning and neurosicence \ psychology. The presentation for the talk may be found here.
Information and learning process
I have recently completed participation in the academic course "introduction to information and learning process" given by (the one and only) Prof. Tali Tishby.
The course introduces the subjects of: Statistical (Bayesian) decision theory, parameter estimation, PAC learning, information theory and some more. I enjoyed the course as it both provides practical intuitions and formal tools to approach problems such as estimation with hidden variables, what are the computational bounds on learning and what are the theoretical constraint on information transfer.
For the sake of future generations, I upload my course materials to this online folder. Especially noteworthy in these are my solutions to questions on: